Bubble Chart Viz

Preparación

Leemos los datos del fichero extraído de Kaggle: https://www.kaggle.com/datasets/anandhuh/covid19-in-world-countrieslatest-data?resource=download.

Acto seguido, preparamos los datos para el grafo deseado.

# Carga y primera lectura (escogemos únicamente los 10 países más poblados).
countries <- read.csv("C:/Users/carlo/OneDrive/Documentos/MASTER/4o semestre/Visualización de datos/PEC2/worldwide covid data.csv")
countries <- head(countries[order(countries$Population, decreasing=TRUE), ], 10)
summary(countries)

##  Country.Other       Total.Cases        Total.Deaths     Total.Recovered   
##  Length:10          Min.   :  168362   Min.   :   3142   Min.   :  141898  
##  Class :character   1st Qu.: 1633154   1st Qu.:  29434   1st Qu.: 1587706  
##  Mode  :character   Median : 5879610   Median : 239761   Median : 5418114  
##                     Mean   :18903629   Mean   : 311538   Mean   :18352255  
##                     3rd Qu.:27142653   3rd Qu.: 484371   3rd Qu.:26162509  
##                     Max.   :82133342   Max.   :1013044   Max.   :80015081  
##   Active.Cases     Tot.Cases..1M.pop Deaths..1M.pop    Total.Tests       
##  Min.   :   2666   Min.   :   117    Min.   :   3.0   Min.   :  4977858  
##  1st Qu.:  13602   1st Qu.:  7923    1st Qu.: 143.2   1st Qu.: 18714172  
##  Median :  50689   Median : 26156    Median : 465.5   Median : 78614352  
##  Mean   : 239836   Mean   : 62472    Mean   :1237.3   Mean   :243996718  
##  3rd Qu.: 375485   3rd Qu.:103429    3rd Qu.:2528.0   3rd Qu.:245050000  
##  Max.   :1105217   Max.   :245579    Max.   :3074.0   Max.   :992445691  
##  Tests..1M.pop       Population       
##  Min.   :  23131   Min.   :1.313e+08  
##  1st Qu.: 113226   1st Qu.:1.795e+08  
##  Median : 208994   Median :2.219e+08  
##  Mean   : 649530   Mean   :4.560e+08  
##  3rd Qu.: 508241   3rd Qu.:3.205e+08  
##  Max.   :2967412   Max.   :1.439e+09

# Observamos los valores únicos de los países resultantes.
countries$Country.Other

##  [1] "China"      "India"      "USA"        "Indonesia"  "Pakistan"  
##  [6] "Brazil"     "Nigeria"    "Bangladesh" "Russia"     "Mexico"

# Mapeamos cada país a su respectivo continente, creando una variable categórica.
countries$Continent <- ifelse(countries$Country.Other=="China", "Asia",
                       ifelse(countries$Country.Other=="India", "Asia",
                       ifelse(countries$Country.Other=="USA", "America",
                       ifelse(countries$Country.Other=="Indonesia", "Asia",
                       ifelse(countries$Country.Other=="Pakistan", "Asia",
                       ifelse(countries$Country.Other=="Brazil", "America",
                       ifelse(countries$Country.Other=="Nigeria", "Africa",
                       ifelse(countries$Country.Other=="Bangladesh", "Asia",
                       ifelse(countries$Country.Other=="Russia", "Asia",
                       ifelse(countries$Country.Other=="Mexico", "America",
                                           NA  ))))))))))

# Factorizamos la variable Continent, para tener la distinción de color en el grafo.
countries$Continent <- as.factor(countries$Continent)
summary(countries$Continent)

##  Africa America    Asia 
##       1       3       6

Visualización

Realizamos la visualización deseada:

if (!require('ggplot2')) install.packages('ggplot2')

## Loading required package: ggplot2

library(ggplot2)

if (!require('plotly')) install.packages('plotly')

## Loading required package: plotly

## Warning: package 'plotly' was built under R version 4.1.3

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(plotly)

# Most basic bubble plot
p <- ggplot(countries, aes(x=Total.Tests, y=Tot.Cases..1M.pop, size = Population, color = Continent)) +
       geom_point(alpha=0.7) + scale_size(range = c(2,10)) 
ggplotly(p)